X-Ray Image Classification and Retrieval Using Ensemble Combination of Visual Descriptors

  • JeongHee Shim
  • KiHee Park
  • ByoungChul Ko
  • JaeYeal Nam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


In this paper, we propose a novel algorithm for the efficient classification and retrieval of medical images, especially X-ray images. Since medical images have bright foreground against dark background, we extract MPEG-7 visual descriptor from only salient parts of foreground. For color descriptor, Color Structure Descriptor (H-CSD) is extracted from salient points, which are detected by Harris corner detector. For texture descriptor, Edge Histogram Descriptor (EHD) is extracted from global and local parts of images. Then extracted feature vector is applied to multi-class Support Vector Machine (SVM) to give membership scores for each image. From the membership scores of H-CSD and EHD, two membership scores are combined as one ensemble feature and it is used for similarity matching of our retrieval system, MISS (Medical Information Searching System). The experimental results using CLEF-Med2007 images show that our system can indeed improve retrieval performance compared to other global property-based or other classification-based retrieval methods.


H-CSD EHD SVM ensemble vector MISS 


  1. 1.
    Qi, H., Snyder, W.E.: Content-based image retrieval in PACS. Journal of Digital Imaging 2, 81–83 (1999)CrossRefGoogle Scholar
  2. 2.
    Mojsilovc, A., Gomes, J.: Semantic based categorization, browsing and retrieval in medical image databases. Int.Conf. on Image Processing 3, 145–148 (2002)Google Scholar
  3. 3.
    Greenspan, H.: Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework. IEEE Transactions on Information Technology in BioMedicine 11, 190–202 (2007)CrossRefGoogle Scholar
  4. 4.
    Bhattacharya, P., Rahman, M.M.: Image Representation and Retrieval Using Support Vector Machine and Fuzzy C-means Clustering Based Semantical Spaces. In: International Conference on Pattern Recognition, vol. 2, pp. 1162–1168 (2006)Google Scholar
  5. 5.
    Manjunath, B.S., Salembier, P., Sikora, T.: Introduction to MPEG-7. John Willy & Sons, LTD. (2002)Google Scholar
  6. 6.
    Harris, C., Stephens, M.J.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–152 (1998)Google Scholar
  7. 7.
    Won, C.S., Park, D.K.: Efficient Use of MPEG-7 Edge Histogram Descriptor. ETRI Journal 24, 23–30 (2002)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  9. 9.
    Chen, S.-C., Murphy, R.F.: A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images. BMC Bioinformatics 7, 1–13 (2006)CrossRefGoogle Scholar
  10. 10.
    Deselaers, T.: The CLEF 2005 Automatic Medical Image Annotation Task. CLEF 2005 74, 55–58 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • JeongHee Shim
    • 1
  • KiHee Park
    • 1
  • ByoungChul Ko
    • 1
  • JaeYeal Nam
    • 1
  1. 1.Dept of Computer EngineeringKeimyung UniversityDaeguKorea

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